Recognition and study of snow reservoirs as the supplier of the base flow of rivers and main outset of freshwater resources in snowy and high basins play an important role in planning and management of water resources usage. However, one of the main problems in snow phenomenon recognition using optical satellite images is to separate clouds and snow. To supper this problem, we use the fact that the cloud does not have a stable geolocation compared to snow. A temporal filter is designed by the combination of Modis Terra and Aqua to remove the cloud pixels. Moreover, different spectral behavior of the cloud in different wavelengths makes it possible to separate it from the snow. A normalized difference cloud index is defined using Modis data to detect and remove the cloud pixels from the image. The pixel-based method is used to extract the snow coverage map of the Northen area of the Fars province using the daily Modis data spanning between 1392 and 1393. In order to evaluate the final results, the data from 14 ground stations as well as Landsat8 OLI image are used as ground truth. The accuracy of 100% was achieved using the first method while the accuracy of the second method by corresponding the pixels of snow coverage maps is estimated as 98. 58%. According to the results and accomplished evaluations, the snow maps generated using the threshold-based method without or with the cloud coverage removed by the application of the proposed method has a high precision. The results can then be easily used in the snowmelt run-off modeling in the water resource and reservoirs management.